Therefore, it has been concluded that, the ERBF performance for SVR model is more suitable in comparison to other two considered kernels for predicting the evaluation of RC frame shear wall buildings. ERBF performed best among all the considered kernels. Polynomial function (PF), Exponential and Gaussian radial basis function (ERBF, GRBF) for SVR modelling. Three kernel parameters have been employed in this study, i.e. IDR, roof displacement, joint rotation and hysteresis energy are considered as the input parameters and GDI as the output parameter in both the perpendicular direction of the RC shear wall building. A total of 176 samples were collected from RC shear wall buildings through nonlinear dynamic analysis (NDA) using SAP2000V21 software, and is used to introduced the SVR model. Hence, in this current study, an effort is made to predict the GDI of RC shear wall buildings using SVR Method. Determination of GDI is done using park and Ang approach but the evaluation is very time consuming, therefore, the application of support vector regression (SVR) method can help in this regards. Evaluation of Global damage index (GDI) of the reinforced concrete (RC) shear wall buildings under seismic conditions through nonlinear dynamic analyses is very important.

Gaussian software competition